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Electric Vehicle Charging System Utilizing a Transformerless Common Mode Voltage Suppression Technique
No full textWith the increasing adoption of electric vehicles (EVs) globally, there is a growing need for more public charging infrastructures, which demands compact designs to minimize their cumulative footprint. Since transformers are key contributors to the overall cost, size, and power losses in charging systems, integrating non-isolated AC-DC and DC-DC converters can potentially result in more economical, compact, and efficient EV chargers. Nevertheless, in the absence of galvanic isolation which is usually provided by isolation transformers, alternative strategies are needed for safety purpose and to mitigate common mode (CM) or ground leakage currents. This article proposes a three-phase transformerless battery charger with buck-boost functionality that significantly reduces ground leakage currents. It features a two-stage conversion architecture: a three-phase front-end T-type converter employing a modified vector modulation and a back-end bidirectional four-switch buck-boost (FSBB) converter operated with a symmetric switching scheme. Collectively, this circuit and its modulation strategies for the active devices produce a low high-frequency CM voltage. This simplifies the filtering requirements for grid and safety compliance while efficiently managing various battery charging profiles. The proposed system and advantages on ground leakage current attenuation is validated through extensive simulations in PLECS and LTSPICE and with a SiC-based experimental demonstrator. The demonstrator shows a significant reduction in leakage current compared to conventional SVPWM in the front-end, along with a synchronous switching scheme in the back-end circuit.</p
Reproducibility of fixed-node diffusion Monte Carlo across diverse community codes:The case of water-methane dimer
No full textFixed-node diffusion quantum Monte Carlo (FN-DMC) is a widely trusted many-body method for solving the Schrödinger equation, known for its reliable predictions of material and molecular properties. Furthermore, its excellent scalability with system complexity and near-perfect utilization of computational power make FN-DMC ideally positioned to leverage new advances in computing to address increasingly complex scientific problems. Even though the method is widely used as a computational gold standard, reproducibility across the numerous FN-DMC code implementations has yet to be demonstrated. This difficulty stems from the diverse array of DMC algorithms and trial wave functions, compounded by the method’s inherent stochastic nature. This study represents a community-wide effort to assess the reproducibility of the method, affirming that yes, FN-DMC is reproducible (when handled with care). Using the water-methane dimer as the canonical test case, we compare results from eleven different FN-DMC codes and show that the approximations to treat the non-locality of pseudopotentials are the primary source of the discrepancies between them. In particular, we demonstrate that, for the same choice of determinantal component in the trial wave function, reliable and reproducible predictions can be achieved by employing the T-move, the determinant locality approximation, or the determinant T-move schemes, while the older locality approximation leads to considerable variability in results. These findings demonstrate that, with appropriate choices of algorithmic details, fixed-node DMC is reproducible across diverse community codes—highlighting the maturity and robustness of the method as a tool for open and reliable computational science.</p
Breast cancer-related fatigue risk and intervention recommendations:What can and cannot be personalised?
No full textCancer-related fatigue (CRF) is one of the most common and underdiagnosed long-term effects after breast cancer. Many factors influence the development of CRF, however, on individual level it is unknown who is going to develop CRF. There are many interventions to reduce CRF, but unfortunately, not a gold-standard intervention that works best for all patients. These both aspects need personalisation, and so the goal of this thesis was to determine what can and cannot be personalised in risks for fatigue and intervention recommendations for breast cancer-related fatigue.In Chapter 2, we studied the personalisation of the risk of developing CRF. It was not possible to accurately predict CRF, as CRF is a complex construct. So, in Chapter 3, focus groups with patients and interviews with healthcare professionals showed the complexity of CRF and the factors that are important to CRF.In Chapter 4, an overview of existing interventions was created, to show the large variation and possibility to give a personalised intervention recommendation. In Chapter 5, breast cancer patients indicated their preferences for interventions and decision rules were developed to create a simple personalised intervention recommendation. In Chapter 6, we tried to predict intervention effectiveness on individual level, again to further personalise the intervention recommendation. As in Chapter 2, it showed to be difficult to predict CRF.In the general discussion of Chapter 7, two themes emerged: the personalisation in predictions in fatigue, and the personalisation of intervention recommendations. For the first theme, improvements of the work of this thesis lies in either the data used, or the modelling approaches. For the second theme, we dived into the extension of the decision rules, and how patients can still receive a personalised intervention recommendation. Future research should focus on the standardisation of data to have a common method to measure CRF, if this is possible at all, and the implementation of the results of this thesis into clinical practice.It can be concluded that based on current available data, personalisation in predictions in fatigue is not accurately possible, while in the personalisation of intervention recommendations, first important steps were made
Cyber physical production system for smart manufacturing analytics and management: a systematic literature review, framework and roadmap
No full textCyber physical production system (CPPS) is a key enabling technology of Industry 4.0 that plays an important role in improving the efficiency, quality, productivity, and sustainability of production systems, resulting in smarter and more responsive manufacturing processes. An in-depth understanding of the multidisciplinary concepts of CPPS is required for quick adoption of CPPS by the industries. This paper presents a systematic literature review on CPPS, analysing 209 identified literatures from 2010 to December 2024 using the PRISMA technique. It proposes a generic CPPS framework to facilitate a comprehensive understanding of multidisciplinary concepts and serve as a roadmap for its effective implementation. Recommendations for future research developments, including innovative concepts, methodologies (tools and techniques), and practices, are explored to bridge the gap between previous studies and emerging research directions. This study serves as a reference in providing researchers and practitioners with valuable insights, knowledge updates, and decision support in selecting CPPS elements and sub-elements according to their impacts and required efforts. Future research recommendations indicate that CPPS should prioritize the deeper integration of digital technologies and artificial intelligence, with a focus on developing sustainable, flexible, and human-centric designs to address evolving industrial needs
Memristor based Gas Sensor:Sensitivity and Timing Analysis
No full textSensors play a critical role in intelligent monitoring systems, particularly in detecting hazardous gas leaks. Accurate detection timing is essential for identifying the causes of accidents and ensuring the safety of appliances. However, conventional gas sensors typically lack onboard data storage and depend on external memory systems. To overcome this limitation, memristor-based gas sensors have gained attention. These advanced devices integrate sensing and memory functions into a single platform, operating as gas-triggered switches with inherent memory capability. This integration reduces overall system complexity and enables real-time data logging directly at the sensor level. In this article, we investigate the use of a memristor device as a gas-sensing element through simulation, demonstrating its ability to detect various gases and their concentrations. We further analyze the sensor's sensitivity and response timing in relation to different gas types and concentration levels. Additionally, we examine the sensing and recovery mechanisms of the memristor-based sensor to understand its performance across different gas environments.</p
Myoback: A Musculoskeletal Model of the Human Back with Integrated Exoskeleton
No full textGiven the challenges of real-life experimentation, musculoskeletal simulation models could become essential in biomedical research. This is especially critical for the human back, a key structure involved in daily movements, where modeling and simulation could streamline design and support the development of treatments and robotic rehabilitation techniques, such as exoskeletons. However, musculoskeletal simulation engines are computationally demanding and lack contact dynamics, restricting current models' use in studying prolonged behaviors or optimizing system design while maintaining physiological accuracy. To overcome this limitation, this work proposes MyoBack, a human back model part of the MyoSuite framework relying on the physics engine MuJoCo. This model is derived from a physiologically accurate model built in the state-of-the-art musculoskeletal simulation software OpenSim and replicates the latter's kinematic properties accurately, with some discrepancies regarding muscle dynamics stemming from engine differences. The MyoBack model was also validated empirically by integrating a passive back exoskeleton in simulation and comparing forces exerted on the back with values from experimental trials. Over different tasks, the model reproduced measured force progressions well, resulting in RMSE =11% for a stoop and RMSE=16% for a squat motion pattern relative to peak forces. The MyoBack model can be accessed here: https://github.com/rohwalia/MyoBack</p
Temporal dynamics of electroconvulsive therapy induced seizures
Full text linkObjective: Electroconvulsive therapy (ECT) is an effective treatment for several psychiatric disorders. The role of cortex and thalamus in seizure expression and termination seems critical. Here we study spatiotemporal dynamics of ECT-induced seizures based on electroencephalogram (EEG) features and biophysical parameters using a corticothalamic mean-field model. Methods: We analyzed 345 ictal EEGs from 33 patients (19 female). EEG features included the dominant frequency, and temporal and spatial correlations. A corticothalamic biophysical model assessed cortical excitation/inhibition (E/I) balance and effective corticothalamic and intrathalamic loop strengths. We tracked the temporal evolution of each feature and parameter from seizure onset to termination. Results: ECT-induced seizures showed EEG slowing (i.e., a decrease in dominant frequency) and increased temporal correlations as seizures approached termination. Cortical E/I ratios and corticothalamic loop strength increased, while intrathalamic strength decreased. Both EEG slowing and increase in temporal correlations were associated with increased cortical E/I ratios and decreased intrathalamic loop strength. Conclusions: ECT-induced seizures show slowing and increased temporal correlations toward termination. These dynamics may be driven by increased cortical E/I ratios and decreased intrathalamic loop strength. Significance: ECT-induced seizures exhibit characteristic temporal corticothalamic dynamics.</p
Wavefront Shaping with Varying Degrees of Freedom
No full textOptical wavefront shaping uses the physical feature that whereas light scattering is complex, it is a linear process and hence deterministic [1], [2]. The incident wavefront is controlled, for instance, to focus light through a scattering sample, by spatially modulating the wavefronts with Spatial Light Modulators (SLMs) or Digital Micromirror Devices (DMDs) combined with Lee holography [3]. The main criterion in traditional wavefront shaping is the enhancement of the intensity at the target, defined as the ratio of the optimized intensity at the target, and the average intensity at the target for many realizations of the scattering sample
Incorporating predictions in online graph coloring algorithms
Full text linkWe focus on learning augmented algorithms for the online graph coloring problem. We consider incorporating predictions in such algorithms to improve their performance. We apply this strategy in particular to the well-known greedy online graph coloring algorithm FIRSTFIT. Although FIRSTFIT is known to perform poorly in the worst case, we are able to establish a relationship between the structure of the input graph G that is revealed online and the number of colors that FIRSTFIT uses for G. Based on this relationship, we propose an online coloring algorithm FIRSTFITPREDICTIONS that extends FIRSTFIT while making use of machine learned predictions. We show that FIRSTFITPREDICTIONS is both consistent and smooth. Moreover, we develop a novel framework for combining online algorithms at runtime specifically for the online graph coloring problem. Finally, we show how this framework can be used to robustify FIRSTFITPREDICTIONS by combining it with any classical online coloring algorithm (that disregards the predictions).</p
Bringing the Land Administration Domain Model to the classroom:A ‘For Dummies’-style introduction to LADM
No full textEarlier this year, FIG published the book titled LADM in the Classroom. In the style of that book, this article gives an overview of the Land Administration Domain Model (LADM), showing how the conceptual model can be applied to different scenarios by way of example. It also provides guidance on how to use LADM in the Classroom and associated materials to further develop LADM competencies